Interpreting Geographical Data

Year one statistics exam

  • Summation sign
  • Rounding
  • Central tendency
  • Variability
  • Boxplots
  • Standard deviations
  • Normal distribution
  • Sampling
  • Reliability and standard errors
  • Confidence intervals and t-distribution
  • Colomn, charts and tables
  • Hypothesis testing and one sample t-test
  • Two sample t-test
  • F-test
  • Anova I
  • Anova II
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  • Created by: Sophie
  • Created on: 04-01-15 13:38

1. Which statement is incorrect about error bars on Excel?

  • You can use the option of standard error or standard deviation in Excel
  • It will either add the wrong error bars or add none at all as does not obtain the whole dataset
  • Make colomn first and then add the error bars yourself
  • Excel only knows the value of the mean and not the other data sets
  • You have to do is calculate the SD, SE or CI yourself and then enter it in this ‘custom’ option, specifying your correct value as both the upward-extending bar (‘positive’) and the downward one (‘negative’).
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2. What is variability?

  • The tendency to vary. It is the measure of variation or dispersion
  • The tendency to differ. It is the measure of difference within in data
  • The tendency to increase. Measure of multiplication
  • The tendency to decline. Measure of division

3. Why are all models wrongs?

  • They are simplifications of reality. However, they may still be useful in helping us make sense of our highly complex world.
  • They are complexities of reality.
  • They summarize the data too much so that the individual values are hard to detect

4. What is the Bonefferoni corection?

  • Substract the critical significance value by the number of hypothesis tests being done. 0.05 - 20
  • Divide the critical significance value by the number of hypothesis tests being done. 0.05 divided by 20 is 0.025
  • Multiply the critical significance value by the number of hypothesis test being down. 0.05 x 20

5. Which is not a feature of residuals?

  • Residual = observed value – fitted value.
  • Residual = observed/fitted value
  • Only rarely are fitted values exactly the same as the observed values. Instead, the model gets it wrong by a certain amount, this is a residual.
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